Daily river flow forecasting in a semi-arid region using twodatadriven

Document Type: Research Paper


1 Range and Watershed Management Dept., Hormozgan University, Bandarabas, Iran

2 Programming and Environment Management Dept., Environment Faculty, University of Tehran, Tehran, Iran

3 Faculty of Natural Resources, University of Tehran, Karaj, Iran

4 Watershed Management Dept., Trabiat Modares University, Noor, Iran

5 Faculty of Agriculture, Engineering, Bahonar Kerman University, Kerman, Iran

6 Department of Natural Resources, Bafgh, Yazd, Iran


Rainfall-runoff relationship is very important in many fields of hydrology such as water supply and water resource
management and there are many models in this field. Among these models, the Artificial Neural Network (ANN) was
found suitable for processing rainfall-runoff and opened various approaches in hydrological modeling. In addition,
ANNs are quick and flexible approaches which provide very promising results, and are cheaper and simpler to
implement than their physically based models. Therefore, this study evaluated the use of ANN models to forecast
daily flows in Bar watershed, a semi-arid region in the northwest Razavi Khorasan Province of Iran. Two different
neural network models, the multilayer perceptron (MLP) and the radial basis neural network (RBF), were developed
and their abilities to predict run off were compared for a period of fifty-five years from 1951 to 2006. The best
performance was achieved based on statistical criteria such as RMSE, RE and SSE. It was found that MLP showed a
good generalization of the rainfall-runoff relationship and is better than RBF. In addition, 1-day antecedent runoff
affected river flow, such that the statistical criteria decreased but the 5-day antecedent rainfall remained unaffected.
Furthermore, considering MLP, RE and RMSE, the best model produced the values 46.21 and 0.75 while the RBF
model recorded 177.60 and 0.82, respectively.


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